Abstract
Forecasting the weather from one month to one season ahead has become very important economically. A clear awareness of the scientific basis of long-term predictive skill began with the work of (1932) and (1969). Seasonal forecasts are possible whenever the chaotic atmospheric motion is perturbed in a predictable way by slowly varying boundary conditions, such as sea surface temperature (SST) or land conditions. The most important of these boundary conditions are the El Niño Southern Oscillation (ENSO) in the Pacific Ocean and the North Atlantic Oscillation (NAO) in the Atlantic Ocean. The El Niño Southern Oscillation is the strongest climate signal in inter-annual timescales (Rasmusson and Carpenter, 1982). It has quasi-periodic behavior with dominant periods of around 2–7 years. Many other similar features distributed around the world have been discovered in recent years. Although the weather is highly non-linear, perturbations to the average weather can often be taken as being proportional to the forcing plus an unpredictable weather noise. This means that simple, often linear, forecast models can be very useful in seasonal forecasting. In fact, statistical models based on the linear El Niño Southern Oscillation, North Atlantic Oscillation, and other teleconnections are used in many locations throughout the world, (Peng and Whitaker, 1999; Wallace, and Gutzler, 1981; Wang, 2001).
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Pokrovsky, O.M. (2009). Self-learning statistical short-term climate predictive model for Europe. In: Global Climatology and Ecodynamics. Springer Praxis Books. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78209-4_8
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